IEEE Transactions on Information Theory
Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
Partial-Update Adaptive Signal Processing: Design Analysis and Implementation
Partial-Update Adaptive Signal Processing: Design Analysis and Implementation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Set-membership binormalized data-reusing LMS algorithms
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
BEACON: an adaptive set-membership filtering technique with sparseupdates
IEEE Transactions on Signal Processing
Partial-update NLMS algorithms with data-selective updating
IEEE Transactions on Signal Processing
Multiweight optimization in optimal bounding ellipsoid algorithms
IEEE Transactions on Signal Processing
On the value of information in system identification-Bounded noise case
Automatica (Journal of IFAC)
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The quasi-OBE (QOBE) algorithm is a set-membership adaptive filtering algorithm based on the principles of optimal bounding ellipsoid (OBE) processing. This algorithm can provide enhanced convergence and tracking performance as well as reduced average computational complexity in comparison with the more traditional adaptive filtering algorithms such as the recursive least-squares (RLS) algorithm. In this paper, we show that the QOBE algorithm is prone to numerical instability due to the unbounded growth/decay of its internal variables. To tackle this problem, we develop a new set-membership adaptive filtering algorithm by transforming QOBE's internal variables into a new set of internal variables. The new algorithm, called modified quasi-OBE (MQOBE), can be viewed as an exponentially-weighted RLS algorithm with a time-varying forgetting factor, which is optimized at each iteration by imposing a bounded-magnitude constraint on the a posteriori filter output error. The proposed algorithm delivers the same convergence and tracking performance as the QOBE algorithm but with enhanced numerical properties. We demonstrate the improved numerical behavior of the proposed algorithm by simulation examples for a MIMO channel estimation problem.